By Louise Dilley, Regional Services Director, Virtual Instruments
Virtual workload balancing is overlooked well past the eleventh hour in many enterprises. This seems counterintuitive, given that enterprises are becoming highly dependent on IT for success. Capacity planning, or workload forecasting, is all about optimisation. It’s the process for determining how capable and prepared the IT infrastructure is to meet future workload demands whilst efficiently managing resources. This process is typically measured within the context of applications that run in rapidly changing, multi layered, virtualized and often cloud based environments. Capacity planning is critical for reducing cost, assuring performance, and increasing business productivity. Proactivecapacity management also saves time and helps to protect against unforeseen challenges caused by workload changes – and saves the urgent need to throw expensive resources at solving resultant performance issues and workload bottlenecks.
Today, capacity planning is a mandatory discipline for enterprises. The problem is that many companies either don’t have the right tools, the right staff expertise, or both. Fortunately, Virtual Instruments has the technology to allow the majority of capacity planning tasks to be automated, doing the work more efficiently than a traditional expert would be capable of. It is now possible to continuously apply smart predictive analytics to optimize applications that run across countless virtualized systems. Automated predictive analytics can help an enterprise to gain a competitive advantage by optimally maintaining the crucial balance between cost and performance. A company can then efficiently meet service levels and availability requirements, without introducing undue risk.
Dedicated capacity planning staff are a rare occurrence now
VirtualWisdom, from Virtual Instruments, is an automated predictive analytics and AIOps platform that produces regular reports that are able to show which of the systems are likely to violate future service levels, as well as when and why. The automated process doesn’t require the prohibitive amounts of up-front capacity planning time or staff expertise. By relying on automated predictive analytics enterprises can be informed of future problems well in advance of their occurrence, providing sufficient insight to enable organizations to avoid issues entirely.
Application performance and utilization is optimized by continuous re-balancing of the underlying infrastructure including VMs, network paths, and storage load distribution. State-of-the art custom analytics use machine learning to analyze VM workload patterns, then recommend the optimal placement of VMware ESX clusters to proactively avoid memory or CPU contention. VirtualWisdom also uses machine learning to monitor storage port utilization and provide recommendations on rebalancing ports on the storage array. Enterprises deploying such tools can reduce their infrastructure costs by 30-50% after deployment by avoiding unnecessary infrastructure spending.
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